Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
On Development and Evaluation of Prototype Mobile Decision Support for Hospital Triage San Pedro, J.a, Burstein, F.a, Wassertheil, J.b, Arora, N.c, Churilov, L.d and Zaslavsky, A.c a School of Information Management & Systems, Monash University, Australia b Faculty of Medicine, Monash University, Australia c
School of Computer Science and Software Engineering, Monash University, Australia d School of Business Systems, Monash University, Australia {frada.burstein, jocelyn.sanpedro leonid.churilov, nitin.arora,
[email protected]}
[email protected] Abstract
Ambiguous triage scenarios in hospital emergency departments are often difficult to assess without decision support. Subjective assessments of such scenarios can either lead to under-triaging or over-triaging for which true conditions of patients are often not addressed within the required time. In this paper, we propose a decision support model that can guide a clinician when identifying the urgency of medical intervention when patient presents with ambiguous triage case. Our model is a heuristic approach that selects the best triage category, identifies corresponding discriminating attribute of the patient, and allows clinician to attach a level of confidence in the decision. We implemented this model as a mobile decision support system, called iTriage. Results of an initial evaluation of iTriage using fourteen paper-based adult triage scenarios showed that our model produced robust decisions for urgent scenarios. For non-urgent scenarios, the proposed model provided guidance especially when the scenarios were ambiguously stated.
1. Introduction Hospital triage aims at categorising the urgency of medical attention needed by patient who presents at emergency departments. The word triage is derived from the French word “trier”, which means, “to sort, pick out, classify or choose” [1]. In emergency departments in Australian hospitals, triage nurses use the Australian Triage Scale (ATS) [2] to guide them through the triage decision making process. The ATS is based on classifying the patient according to eight physiological attributes, namely, airway, breathing, circulation, conscious state, pain, neurovascular status, mental health emergencies and ophthalmic emergencies (see Appendix Table 1). For example, a patient who presents at the emergency
department with obstructed airway will be assessed under triage category 1. In this category, the patient will be advised to see the doctor immediately (see Appendix Table 2). The ATS uses linguistic terms such as immediately or imminently life-threatening; potentially life-threatening or life-serious. The meanings of these terms are only understood by clinicians or triage nurses sharing common domain knowledge. The physiological attributes in Table 1 also uses linguistic terms such as mild, moderate, pink, pale, etc. Despite common understanding of terms, there still are vague distinctions between mild and moderate or pink or pale in that some patients with these attributes may be classified ambiguously under two or more triage categories. Such ambiguous cases often lead to under triage or over triage decisions, instead of correct decisions. An under-triage decision occurs when a patient has to wait longer for medical attention than anticipated. An over-triage decision, on the other hand, occurs when a patient has to wait for medical intervention for shorter period than demanded. A correct triage therefore is an appropriate allocation of triage category in which a doctor sees the patient within a suitable time frame. For example, the following scenario is taken from a recent study on consistency of triage in Victoria [3]: Fifty-year-old male presents with a workmate with a laceration to his right hand. He is able to walk to the triage desk unassisted. He was using an electric saw and has a 4cm laceration to his right index finger. His respiratory rate is 22 with no use of accessory muscles and his oxygen saturation is 99% His heart rate is 68 (regular), and his skin is pale, cool and dry His blood pressure is 135/85 His GCS [Glasgow Coma Scale] is 15 He is complaining of pain in his finger 3/10 He is unable to move his right index finger and complains of altered sensation to the finger tip
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His laceration is not bleeding His temperature is 36.5 The expected triage category for this scenario is Category 4. Out of 167 nurses who participated in the study, nearly 50% of nurses correctly triaged the patient while about 44% over triaged the patient. Of the 11 cases where under triaging took place, all were within one category of the expected triage category. Of the 73 cases over triaged, 71 were within one category of the expected triage category and two were within two categories. The above results showed evidence of ambiguity in the description of the scenario. The study however, did not report on the cause of inconsistency in the triage decisions. If details of triage decision-making process were collected during the study, information about the cause of inconsistency could have been identified and addressed to inform future triage decision-making. Without decision support, triage nurses and clinicians face difficult task of assessing the urgency of medical attention required by patients. Decision support tools can be provided to aid the nurses when triaging patients, especially during ambiguous cases. These tools may include paper-based or computer-based ATS forms which nurses use to guide them through triage decision-making process. With the advent of the Internet and new wireless technology, the ATS forms can now be made available online via handheld devices such as Personal Digital Assistants (PDA), notebooks or laptops, or mobile phones. There are a whole range of advantages for making ATS available online via handheld devices. The most significant is that of providing real-time decision support. Faster collection, storage, access and retrieval of patient electronic records anywhere, anytime are the most elementary levels of decision support one can get from mobile devices. In our current project, we aim at increasing the level of decision support that decision makers can receive from their mobile devices. In this paper, we present a decision support model that aims at reducing the ambiguity in assessing triage patients when they present at hospital emergency departments. Our model is a heuristic approach that selects the category that best classifies patient’s need for medical attention, identifies the attribute that clearly identifies such need, and allows the triage nurse to assign level of confidence in his/her triage decision. A prototype mobile decision support iTriage that implements the proposed decision support model is also described. In the next section, we present this heuristic approach. We then discuss in the succeeding sections some issues relating to the design, development, implementation and initial evaluation of iTriage. In the last two sections, we conclude the paper with the discussion of the evaluation results and conclusions.
2. Heuristic Approach to Hospital Triage In practice, a nurse goes through the eight physiological attributes in order of importance. Referring back to Table 1, we can imagine a nurse ticking the appropriate items from left to right, and then identifying the corresponding category in the first column. The overall triage after this thorough process will be taken as that corresponding to highest level of urgency. It will then be just be a matter of making this ATS form available online either in paper-based format or in computer-based format on desktop, notebook or laptop, or PDA. In developing the prototype system, we chose to make ATS form available via PDA. This is because the PDA are now used very widely in healthcare industry due to their relatively lower cost and grater availability over tablet PCs or laptops. However, because of the size of the screen of PDA, we had to consider some design issues when making ATS form available online. One issue relates to the presentation of the eight physiological attributes simultaneously to the clinician or nurse. We had to consider grouping the attributes by two, and presenting each group in a 4-page window. We also needed to develop a decision support model that would assist the nurse in using the ATS when the 8 attributes are not presented altogether in one window. In developing the decision support model, we considered three strategies for assisting a nurse through triage decisionmaking process. These strategies are briefly described below and described in detail in [4]. Strategy 1: Allow the nurse to select a category based on existing guidelines and rules with a possibility of overwriting the result based on his/her expert judgment. This corresponds to the cognitive process of “knowing” based on undifferentiated familiarity and retrieval of specific contextual and conceptual information [5, 6]. For an expert triage nurse faced with ambiguous triage cases, this strategy will yield a robust decision, because the decision can be established quickly. Strategy 2: Allow the triage nurse to consider the succeeding attributes until a unique or higher category is established. This corresponds to the “Take-The-Best” (TTB) heuristic [7] that is based on the concept of bounded rationality. In TTB method, given a set of cues (object attributes) ordered according to their decreasing validity, “if the best (i.e., the most valid) cue discriminates, then the object favored by this cue is chosen, and further search is terminated” [8].
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Strategy 3: Allow the nurse to differentiate two categories based on the same attributes by modifying attribute values to reflect which category is better. For example, a patient who is experiencing mild pain may be assessed under categories 4 and 5. To differentiate these two categories, a 10-point scale is sometimes used by nurses to assess the patient’s pain. For example, scores from 0 – 2 correspond to mild pain, 3-6 correspond to moderate pain, and 7-10 correspond to severe pain. We can also allow the use of fuzzy scores [9] to allow the nurse to assess pain, skin status, and other attributes to convert the linguistic terms used in Table 1 to numerical scale. The use of fuzzy scores requires the construction of fuzzy membership functions, which only domain experts can provide. Even so, there still is a need for domain experts such as triage nurses and clinicians to have common understanding of these linguistic terms and their numerical equivalent.
3. Prototype Mobile Decision Support System: iTriage We employed the prototyping approach to developing the system to allow us frequent interactions and feedbacks from target users. Of the three strategies, we only implemented Strategy 2 in our initial prototype. We expect to encounter several issues, including that of ethical nature that may be involved when implementing Strategy 1. We also recognise the need for careful construction and validation of fuzzy membership scores with the domain experts before they can be implemented as the basis for Strategy 3. Thus the system, called iTriage, as shown in Figure 1 only implements Strategy 2. We hope to implement the remaining strategies after assessing the validity of Strategy 2 in terms of its capability to support triage decision-making.
We illustrated the triage workflow in Figure 1 as implemented in iTriage. In Figure 1a, the nurse will be asked to logon with his/her username and password. This figure shows three algorithms for triage: Hospital triage, Sort and Sieve. For the purpose of this paper, we will only discuss hospital triage. The next screen (Figure 1b) presents the first group of physiological attributes to be considered for triage assessment. In the absence of proper grouping of attributes, the attributes were considered in pairs, according to column order in Table 1. In this screen, the nurse is also presented with slide bar to allow him/her to estimate his/her level of confidence (CF) in her decision for the given attribute. The three boxes on the lower left of the screen calculate the highest category across all attributes so far considered in the assessment, the corresponding level of confidence and the discriminating attribute corresponding to the highest category. The next screen on Figure 1c shows the attribute values for breathing in a list box. The nurse can select the description that best describes the patient. Note that these attribute values are exactly those described in Table 1. When assessing a patient with patent airway, a new window (see Figure 1d) will appear to allow the nurse to select the best category from among four choices. Because this attribute value corresponds to multiple categories, a free-text box is provided to allow the nurse to provide his/her reasons for his/her decision. It should be noted however, that this free-text box is not enabled yet. We suspect that by requiring the nurse to justify a decision might delay the triage process. The next screen in Figure 1e prompts the nurse to save the decision when Category 1 (or highest) category is established, while the screen on Figure 1f corresponds to the recommended action corresponding to the triage decision.
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(a) Welcome menu
(b) Page 1 – airway and breathing attributes
(c) Attribute values for breathing attribute
(d) Multiple categories for patent airway
(e) Final decision reached when highest category is established
(f) Triage decision
Figure 1. iTriage prototype mobile decision support system
3.1 Saving Triage Decisions Every time the nurse saves a triage decision it gets recorded into a flat file on his/ her handheld device. This flat file can be synchronized onto a centralized machine (server) that is part of the hospital emergency department. Once the file has been synchronised a batch file is executed which then is used for automatically updating the corresponding database tables residing on the central machine/server.
Every triage record is made up of two ‘mini’ records. The first set of values consists of the initial triage decision made by the nurse. The structure of the first set of values is as follows: Date and time the decision was taken Nurse Login ID Patient Name Set of category values corresponding to 8 physiological attributes. Final physiological attribute Overall confidence factor.
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The structure of the second set of values is as follows Date and time the decision was taken Logon ID of the nurse Patient Name Overwritten category value Overwritten final physiological attribute Overwritten confidence level. The nurse has the option to overwrite his/her decision. The overwritten set of values forms the second part of the saved record. If the confidence level indicated by the nurse is 100% (in which case the system will not prompt for an overwrite) or if the nurse does not wish to overwrite his/her initial decision the second set of values are the same as the first set. The reason behind storing the same set of values twice is to maintain consistency of records. In addition these are also useful for statistical purposes (e.g. the number of times a given nurse changed her decision etc.).
3.2 Databases
Field name Cat Recomm
Description Logon Id of the nurse/clinician Last name First name Password associated with the login ID (currently supports no encryption)
Physiological_Discriminators- This table is used to store the various categories and attributes as defined by the ATS. These form the set of rules, which govern a triage process. The table structure is as follows: Field name Id Attribute Cat Value
Description Primary key of the table. Not directly applicable. Physiological discriminator(s) as defined in the ATS Category value associated with the attribute Value associated with the attribute and its corresponding category
Description Category values (cat 1 – 5 as defined by ATS) Recommendation for the associated category
The second set of database tables reside on a designated centralized machine, which is part of the emergency department’s Hospital Information System (HIS). These tables are used primarily to store the records of all the previous triage decisions made across the emergency department. The tables are as follows: Triage_Records – used to store all the triage decisions made by the nurse. The structure of the table is as follows:
The system uses databases for all its data needs. This not only makes the system flexible and easy to maintain but also allows it to be scalable as any changes to the database values are automatically reflected in the application. The triage system is made up of two sets of databases. The first database is local to the handheld device and is made up of the following set of tables Nurses - This table is used to store the login information of the various nurses/clinicians who are allowed to use the system. The table structure is as follows: Field name id lst_name fst_name Pwd
Recommendations - This table is used to store the set of recommendation for each of the 5 categories as defined by the ATS. The structure of the table is as follows:
Field name Time Nur_lgn_id Pat_name Attrib_val
Category Cf
Description Date and time the decision was made ID of with the nurse who made the decision Patient name category values corresponding to each of the 8 physiological attributes; If a category for a particular attribute is not defined by the nurse it automatically get defaulted to 0. Highest category achieved. Overall confidence level
Triage_Decisions – stores all the decisions which were over-written by the nurse. The structure of the table is as follows: Field name time Nur_lgn_id Pat_name Attrib_val Category cf
Description Date and time the decision was made ID of with the nurse who made the decision Patient name Overwritten attribute value Overwritten category value Overwritten confidence factor value
These tables correspond to the current structure of the flat file. As mentioned earlier, every record in the flat file is made up of two parts – the first, comprises the values for the initial triage decisions and the second part is used to store the overwritten values of this decision. Thus, every time a nurse/clinician synchronises his/her handheld device with the HIS, a batch file is executed.
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The tables are then automatically updated with the corresponding sections of the records.
4. Results of the Initial Evaluation of iTriage A preliminary evaluation of iTriage was conducted to allow validation of the proposed model in terms of its capability to provide decision support. We also aimed at looking for any problems that a clinician might encounter while using iTriage when assessing adult triage scenarios. Fourteen paper-based scenarios, taken from the recent study [3] were used for this purpose. Traditional paper based scenarios have been widely used when comparing triage decisions [10, 11]. Although seven of these scenarios were presented to the participants in [3] using computer-based formats (with description and still pictures of patients on the computer screen), during our initial evaluation of the system we had presented all scenarios in the traditional paper-based format. This was done because our intention at this stage was not to conduct a comprehensive usability test of triage decisions. Instead the aim was limited to initial testing of the system by one clinician in order to get his feedback and seek recommended changes to the system to address the problems encountered during the test before a formal clinical assessment is conducted. The scenarios used in the initial evaluation contained the following information: patient age, heart rate, patient gender, skin status, mode of arrival, neurological status, presenting problem, pain status, respiratory rate, temperature, use of accessory muscles, past medical history and medications, and oxygen saturation. The full details of the scenarios can be found in [3], also available online in http://www.med.monash.edu.au/healthservices/CNR/Con sistency%20Report%2006-09-01.pdf. The results of the initial evaluation are summarized in Table 3. Of the 14 scenarios, all those with expected categories of 1 and 2 were correctly triaged. The clinician agreed that iTriage assisted him to produce robust category 1 and 2 decisions. Ambiguous cases were detected in scenarios that had expected categories of 3, 4 and 5. This observation was consistent with high percentage of over- triaging for these scenarios in [3]. The observed causes of inconsistency in the decisions of the clinician were (1) the subjective assessment of pain and multiple interpretations of score for pain, (2) default setting of highest category of 4 when pain is assessed as mild, and (3) confusion regarding the relevance of mental
health emergencies in non-psychiatric cases. As seen in Table 3, there were several occasions when the clinician associated the score of 3/10 for pain for both mild and moderate pain. The other cause of inconsistency could be due to the default setting of category 3 when pain is assessed as mild. The clinician in several occasions was always prepared to accept the default setting, which unfortunately led to over-triaging. To help reduce the ambiguity of cases, we proposed some recommendations for the next iterate of iTriage. These recommendations are summarised in the last column of Table 3. We also observed some problems encountered by the clinician when using iTriage. These problems and some recommendations to address these problems include:
Default setting of 0% for confidence level – the clinician needed to slide the bar from 0% to 100% when considering each of 8 physiological attributes. This not only delayed the decision making process, but also did not reflect current practice. The clinician should be expected to be confident in his/her decision most of the time, and should need to provide lower confidence only when triage scenarios become very difficult or ambiguous.
When airway is patent, the algorithm selects the highest category of 2 out of four choices of 2,3,4,5. The clinician found this option irrelevant as the nurse would still need to validate his/her decision by considering the remaining 7 physiological attributes. Thus, we recommend for future iterates of iTriage to allow the nurse to continue the triage without further delay if airway is patent, and exit with Category 1 decision when airway is obstructed or partially obstructed.
In calculating the final triage decision, the system employs a max-max algorithm for selecting the highest category. That is, for each of 8 attributes, the highest category is selected by default. Then the highest category is calculated across all attributes to produce the final decision. This max-max procedure is observed to prevent the system from producing the correct triage when over triaging occurs in one or more attributes. It was suggested therefore that a max-min algorithm be used instead to allow the nurse to still assign a lower category based on remaining attributes, to validate her decision when there is a danger of over triaging, and allow selection of highest category only in the final triage selection.
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Table 3: Results of initial evaluation of iTriage Scenario
1 2
Expected Triage Category 2 5
Actual Triage Decision 2 5
Observations/Comments
good; robust decision no option for “no pain” – left blank
3
3
3
during trial airway patent defaults to highest category 2 (out of choice 3,4,5) time wasted when assigning confidence level > 0% score of 5/10 for pain was subjectively assessed as moderate with low confidence migraine and depression were assessed as mental distress
4
1
1
category 1 was easily established using
5
4
3 – trial 1
missing option for “no pain” – left blank
Recommendations
include option for “no pain” exit only when category 1 is
established confidence level should be set
to 100% by default
mental health emergencies
should be taken in the context of psychiatric distress
attribute: GCS =17 during trial over triage in trial 1 – agrees with
clinical trial [3] 2 – trial 2
clinician used ATS to produce expected
triage 6 7
3 3
3 4 – trial 1 3 – trial 2
score of 3/10 for pain was assessed as
mild Score of 3/10 for pain was assessed as moderate
the expected triage for this
scenario do not conform to ATS; there is need to enforce ATS where possible misinterpretation of scenario could lead to incorrect triage include the following score for
pain in next prototype 0 - 2 mild 3 - 6 moderate 7 - 10 severe allow fuzzy scores in future
prototypes good to provide option to revise/confirm
decision when confidence is not 100% 8
5
5 – trial 1
mental distress not taken in context of
4 – trial 2
mental health irrelevant
2 3 1 3 – trial 1
score of 3/10 for pain was taken as
psychiatric distress
9 10 11 12
2 3 1 4
should allow to revise/confirm
decision when confidence level is below 100% (e.g., 80%) should provide choice to disable/enable mental health emergencies when irrelevant/relevant
moderate pain with low confidence not enough information about skin
status 4 – trial 2
score of 3/10 for pain was taken as
mild 13
4
3
over triaged because score of 4/10 was
14
3
3
missing option for “no pain” – left blank
interpreted as moderate during trial
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5. Conclusions
6. References
Use of mobile devices is becoming more and more widespread in health care context. Availability of the personal device to support triage nurses in making decisions in the emergency department provides a few opportunities for improving the level of patient care, including consistency and timeliness of decisions regarding future treatment. The advantage of having such a system on a PDA rather than a desk top computer is that it can be used any time and any where for real time decision making. The use of PDA is now prevalent in the healthcare industry and they are cheaper to implement than notebooks or laptops. For large-scale clinical testing or deployment of the system, we can expect the government to be able support funding of several units of PDAs instead of the more expensive desktops, wireless notebooks or laptops. In this paper we have described how PDA can be used for guiding nurses through Australian Triage Scale (ATS) in supporting their decision making process about the urgency of medical assistance. We proposed three possible strategies for solving this problem and demonstrated feasibility of implementing one of those strategies as a mobile PDA-based prototype named iTriage. Based on initial assessment of the system by an experienced clinician, we can report that its performance results were encouraging enough to conclude that such system can potentially be of use in the proposed context. It can also be included in the suit of intelligent support systems, which the nurses require when they need to get access to most recent guidelines and other best practice knowledge bases. Opportunity to store decisions at the time they are made together with the reasons for such decisions can potentially provide a source for discovering new knowledge and sharing it for future improvements of existing practices. However, without actual field-testing, we cannot support our claims. We are currently developing a fullscale evaluation and field testing procedure for the proposed technological approach. It will be based on an improved version iTriage as discussed in this paper. We plan also to assess the impact of using computerised decision support model during actual triage. As far as the current literature review reports our study will be one of the few empirical testing of decision support systems in this context.
[1] Victorian Department of Human Services, Consistency of Triage in Victoria's Emergency Departments: Summary Report, Melbourne, Australia, 2001. [2] Victorian Department of Human Services, Consistency of Triage in Victoria's Emergency Departments: Guidelines for Triage Education and Practice, Melbourne, Australia, 2001. [3] Victorian Department of Human Services, Consistency of Triage in Victoria's Emergency Departments: Triage Consistency Report, Melbourne, Australia, 2001. [4] San Pedro, J. , Burstein, F., Churilov, L., Wassertheil, J. and Cao P., “Intelligent Multi-Attribute Decision Support Model For Triage” to appear in Proceedings of the International Conference in Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU2004) , Perugia Italy, July 4-9, 2004. [5] Mandler, G. (1980). Recognizing: The judgment of previous occurrence. Psychological Review, 87, 252-271. [6] Mandler, G. (1991). Your face looks familiar but I can’t remember your name: A review of dual process theory. In W.E. Hockley & S. Lewandowsky (Eds.), Relating theory and data: Essays on human memory in honor of Bennet B. Murdock (pp. 207-225). Hillside, NJ: Erlbaum [7] Gigerenzer, G., Hoffrage, U. and Kleinbolting, H. “Probabilistic mental models: A Brunswikian theory of confidence”, Psychological Review, 1991, Vol 98, pp. 506528. [8] Broder, A., “Assessing the empirical validity of the “TakeThe-Best” heuristic as a model of human probabilistic inference.”, Journal of Experimental Psychology: learning, memory, and Cognition, 2000, 26(5), 1332-1346. [9] Zadeh, L.A., “Fuzzy Logic, Neural Networks, and Soft Computing”, Communication of the ACM, 1994, 37 (3), 7784 [10] Jelinek G, Little M. “Inter-rater reliability of the National Triage Scale over 11,500 simulated occasions of triage”, Emergency Medicine 1996, Vol 8, pp. 226-230. [11] Whitby S, Ieraci S, Johnson D, and Mohsin M. Analysis of the process of triage: the use and outcome of the National Triage Scale, Liverpool Health Service, Liverpool, 1997.
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Appendix Category
1
Air Way
Obstructed Partially obstructed airway
Table 1: Physiological attributes for Adult Triage (adapted from [2]) Mental Health Disability Disability – Breathing Circulation Disability Emergencies Pain Neurovascul – ar Status Conscio us State Absent respiration or hypoventilati on Severe respiratory distress, e.g. Severe use accessory muscles Unable to speak Central cyanosis Altered conscious state
2
3
Patent airway
Patent airway
Moderate respiratory distress, e.g. Moderate use accessory muscles Speaking in words Skin pale/peri pheral cyanosis
Mild respiratory distress, e.g. Mild use accessory muscles Speaking in sentences Skin pink
Absent circulation
GCS=13
Ophthalmic Emergencies
Severe pain, e.g. Patient reports severe pain Skin pale, cool Severe alteration in vital signs Requests analgesia
Severe Neurovascular compromise, e.g. Pulseless Cold Nil sensation Nil movement Decreased capillary refill
Moderate pain, e.g. Patient reports moderate pain Skin pale, warm Moderate alteration in vital signs Requests analgesia
Moderate Neurovascular compromise, e.g. Pulse present Cool Decreased sensation Decreased movement Decreased capillary refill
Probable risk of danger to self or others Attempt/threat of self harm Threat to harm others Severe behavioral disturbance, e.g. Extreme agitation/restless ness Physically/verball y aggressive Confused/unable to cooperate Requests restraint Possible danger to self or others, e.g. Suicidal ideation Severe distress Moderate behavioral disturbance, e.g. Agitated/restless Intrusive behavior Bizarre/disordere d behavior Withdrawn Ambivalence re Tx
Penetrating eye injury Chemical injury Sudden loss of vision with or without injury Sudden onset severe eye pain
Sudden abnormal vision with or without injury Moderate eye pain, e.g. Blunt eye injury Flash burns Foreign body
Psychotic symptoms, e.g. Hallucinations Delusions Paranoid ideas Affective disturbance, e.g. Symptoms of depression Anxiety Elevated/irritable mood
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4
Patent airway
5
Patent airway
No respiratory distress, e.g. No use accessory muscles Speaking in full sentences
No respiratory distress, e.g. No use accessory muscles Speaking in full sentences
No haemodynamic compromise, e.g. Palpable peripheral pulses Skin pink, warm, dry
No haemodynamic compromise, e.g. Palpable peripheral pulses Skin pink, warm, dry
Normal GCS Or no acute change to usual GCS
Normal GCS Or no acute change to usual GCS
Mild pain, e.g. Patient reports mild pain Skin pale/pink, warm Mild alteration in vital signs Requests analgesia
Mild Neurovascular compromise, e.g. Mules present Warm Decreased/nor mal sensation Decreased/nor mal movement Normal capillary refill
Mild pain, e.g. Patient reports mild pain Skin pale/pink, warm No alteration in vital signs Declines analgesia
No Neurovascular compromise
Moderate distress, e.g. No agitation/restless ness Irritable not aggressive Cooperative Gives coherent history Symptoms of anxiety or depression without suicidal ideation No danger to self or others No behavioral disturbance
Normal vision Mild eye pain, e.g. Flash burns Foreign body
Normal vision No eye pain Foreign body Red eye
No acute distress, e.g. Cooperative Communicative Compliant with instructions Known patients with chronic symptoms Request for medication Minor adverse effect of medication Financial/social/ accommodation/ relationship problem
Table 2: Australasian Triage Scale Categories (adapted from [2]) ATS Category 1 2 3
4
5
Description of Category Immediately life-threatening Imminently life-threatening; important time-critical treatment; very severe pain Potentially life-threatening; situational urgency; human practice mandates the relief of severe discomfort; distress within 30 minutes Potentially life-serious; situational urgency; significant complexity or severity; human practice mandates the relief of severe discomfort; distress within 60 minutes Less urgent; clinic-administrative problems
Response Immediately Assessment and treatment within 10 minutes Assessment and treatment start within 30 minutes Assessment and treatment start within 60 minutes Assessment and treatment start within 120 minutes
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